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Validation of AI Models to Measure Physical Activity After a Stroke

Validation and Testing of Artificial Intelligence Models to Measure Physical Activity in Patients Admitted to Hospital Following a Stroke

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06030323
Acronym
CaRRAT
Enrollment
34
Registered
2023-09-11
Start date
2023-06-24
Completion date
2024-06-24
Last updated
2023-09-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Stroke

Brief summary

The research team are developing algorithms using artificial intelligence that use information collected by accelerometers to detect a person's position, such as whether an individual is lying, sitting, or standing, and the individual's movements, such as whether they are taking steps or standing up. Sensor location will affect the accuracy of the model and acceptability of the method. The research team are therefore developing algorithms for four different locations. The purpose of the research is for the development of the algorithms and check whether they accurately recognise different positions and movements in people whose movement is affected by a stroke, and by being in a hospital environment (e.g. using a profiling bed). The research team plan to recruit between 34 and 50 participants who are admitted hospital due to having a stroke. After providing informed consent, participants will be asked to complete a one-off assessment with a member of the research team and a ward physiotherapist. Participants will be asked to wear the four sensors, and move through a series of postures, walk for up to six minutes, and stand as many times as they feel able in one minute.

Interventions

OTHERNo intervention

This is not an interventional study. The study is classified as 'pre-clinical device development or performance testing'. The purpose of the study is to develop and validate the AI models using data collected from people affected by stroke in a hospital environment, to test the accuracy of the optimised models, and to ascertain patient preference for sensor location.

Sponsors

Cambridge University Hospitals NHS Foundation Trust
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* admitted to hospital with a diagnosis of an acute stroke.

Exclusion criteria

* unable to provide informed consent; * receiving end-of-life care; * the consultant in charge of their care disagrees with their inclusion

Design outcomes

Primary

MeasureTime frame
Sensitivity and specificity of AI modelsCollected during hospitalisation (up to 12 weeks)

Countries

United Kingdom

Contacts

Primary ContactPeter Hartley, PhD
peter.hartley@nhs.net441223596317

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026